Amplitude modulation features for emotion recognition from speech
Bibliographic record
Abstract
The goal of speech emotion recognition (SER) is to identify the emotional or physical state of a human being from his or her voice. One of the most important things in a SER task is to extract and select relevant speech features with which most emotions could be recognized. In this paper, we present a smoothed nonlinear energy operator (SNEO)-based amplitude modulation cepstral coefficients (AMCC) feature for recognizing emotions from speech signals. SNEO estimates the energy required to produce the AM-FM signal, and then the estimated energy is separated into its amplitude and frequency components using an energy separation algorithm (ESA). AMCC features are obtained by first decomposing a speech signal using a C-channel gammatone filterbank, computing the AM power spectrum, and taking a discrete cosine transform (DCT) of the root compressed AM power spectrum. Conventional MFCC (Mel-frequency cepstral coefficients) and Mel-warped DFT (discrete Fourier transform) spectrum based cepstral coefficients (MWDCC) features are used for comparing the recognition performances of the proposed features. Emotion recognition experiments are conducted on the FAU AIBO spontaneous emotion corpus. It is observed from the experimental results that the AMCC features provide a relative improvement of approximately 3.5% over the baseline MFCC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".